Gu3311901196

Dharam Vir, Dr. S.K.Agarwal, Dr. S.A.Imam / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.1190-1196
1190 | P a g e
Productivity Analysis of Reactive Routing Protocol for IEEE
802.11e standard using QualNet Simulator
Dharam Vir*, Dr. S.K.Agarwal**, Dr. S.A.Imam***
*Department of Electronics Engineering, YMCA University of Science & Technology, Faridabad, India
**Department of Electronics Engineering, YMCA University of Science & Technology, Faridabad, India
***Department of Electronics & Comm. Engineering, Jamia Millia Islamia, New Delhi, India
ABSTRACT
The continuous improvement of sensor
skill and wireless communication is encouraging
wireless sensor networking. The IEEE 802.11e
Medium Access Control (MAC) is upcoming
standard of IEEE to support Quality of Service
(QoS). The IEEE 802.11e MAC enhances the
basic 802.11 MAC to provide quality-of-service
support for audio and video streams. The 802.11e
MAC defines a new Hybrid Coordination
Function (HCF), which provides an Enhanced
Distributed Channel Access (EDCA) method and
an HCF Controlled Channel Access (HCCA)
method. Some recent work can prove that 802.11e
Hybrid Coordinate Function (HCF) can improve
significantly the quality of service (QoS) in 802.11
networks. The HCF scheduling algorithm is only
for Constant Bit Rate (CBR) characteristics.
In this paper we investigate the performance of
802.11e through computer simulations. We design
a scenario and analysis the performance metrics
of Ad-hoc On-demand Distance Vector (AODV)
and Dynamic Source Routing (DSR) protocols for
IEEE 802.11e standards. Performance metrics
like throughput, average end-to-end delay,
average jitter, energy consumed in transmit and
received modes, which is carried out using
QualNet simulator.
Keywords - AODV, DSR, Average End-to-end
delay, IEEE 802.11e standard, QualNet
I. INTRODUCTION
In recent years, the progress of
communication technology has made wireless
devices smaller, less expensive and more powerful.
The IEEE 802.11 as the standard for wireless local
area networks (WLANs) has given reliability to the
concept that WLANs may soon form a large part of
multi service communication networks [1]. IEEE
802.11 based wireless local area networks have
become more and more popular due to low cost and
easy deployment, they can only provide best effort
services and do not have quality of service supports
for multimedia applications. Recently, a new
standard, IEEE 802.11e, has been proposed, which
introduces a so-called hybrid coordination function
(HCF) containing two medium access mechanisms:
contention based channel access and controlled
channel access. The primary obstacle to the use of
IEEE 802.11 in multi service wireless networks is a
need of quality-of-service (QoS) functionality that is
demanded by real-time voice and video applications.
To overcome this, the IEEE 802.11 Working Group
created Task Group E to design medium access
control (MAC) layer QoS enhancements to the
802.11 standard [2]. The attraction of the 802.11e
standard is the hybrid coordination function (HCF).
HCF provides an efficient mechanism for centrally
coordinated medium access and uses the enhanced
distributed coordination function (EDCF) for
distributed coordination of medium access. EDCF
provides service differentiation amongst different
traffic priorities and is backward compatible with
legacy 802.11 DCF [3].
We illustrate that the 802.11e standard
provides a very powerful platform for QoS supports
in WLANs. The IEEE 802.11 standard adopts the
802.11e as the medium access protocol and
802.11n-based products, which utilize multiple-
input-multiple-output (MIMO) transmission
systems. The medium access collision avoidance of
IEEE 802.11e, even with IEEE 802.11n at the
physical layer, still utilizes the enhanced distributed
coordination function (EDCF) mechanism. It is
known that collisions cause significant performance
degradation in IEEE 802.11e EDCF-based systems
[3][4]. In this paper we focus on the performance of
AODV and DSR reactive routing protocol using
QualNet 5.0 simulator for IEEE 802.11e MAC,
PHY and Energy model WLAN standards. The rest
of the paper is organized as follows. The overview
of IEEE 802.11e PHY/MAC and Energy Models are
summarized in section II. Brief description of
AODV and DSR reactive Routing Protocol
discussed in section III and in section IV
Implementation of related work and simulation
results and conclusion in section V.
II. OVERVIEW OF IEEE 802.11E PHY/MAC
AND ENERGY MODELS
In 1997, IEEE standardized the first
Wireless Standard: 802.11. This comprised both
Medium Access Control (MAC) layer and Physical
layer. While the IEEE 802.11e standard and all the
later extensions provide extensive information
regarding different aspects of the communication, in
this section we briefly describe the concepts in
MAC layer, Physical layer, Propagation Model and

1191 | P a g e
Energy Model for an extensive behavior of the
standard (MAC and PHY layers) [5].
A. IEEE 802.11e Overviews:
IEEE 802.11e is an enhanced version of the
802.11 MAC in order to support quality of service
(QoS). IEEE 802.11e supports quality of service by
introducing priority mechanism. All types of data
traffic are not treated equally as it is done in the
original standard, instead, 802.11e supports service
differentiation by assigning data traffic with
different priorities based on their QoS requirements.
IEEE 802.11e introduces a new coordination
function, called Hybrid Coordination Function
(HCF), to provide QoS support. Subsequent sections
describe HCF together with the detailed description
of its service differentiation mechanism [6].
MAC layer, as its primary purpose, has the
functionality of providing reliable data delivery
mechanism over the unreliable wireless air interface.
It is the layer who manages station access to the
shared wireless medium. The original standard
utilizes Carrier Sense Medium Access with
Collision Avoidance (CSMA/CA) as the access
mechanism. Point Coordination Function (PCF):
It is another basic coordination function
which is defined only in infrastructure mode, where
stations are connected to an access point. Access
point is the element in control of access in the
network and it uses two periods to enforce its
policies. There is a Contention Period, in which,
DCF method is used. The second period is the
Contention Free Period, in which AP basically
allows stations, by sending them a special
authorization, to send packets. IEEE 802.11e
standard addressed the existing limitations in DCF
and PCF. It particularly addressed the problem of
QoS provisioning in the network by introducing a
new coordination function: Hybrid Coordination
Function – HCF [6][7].
Figure1. Overall view of IEEE 802.11e Modelling.
1) Enhanced DCF Channel Access (EDCA):
EDCA is an IEEE 802.11e method of
channel access within the HCF. An EDCA is
basically a QoS enabled DCF. This is done by
introducing the notion of traffic classes, by giving
priority, in channel access, to real-time data,
compared to delay-tolerant data [8].
HCCA-IEEE 802.11e:
The EDCA is a QoS enabled PCF. It also
uses EDCA during the Contention Period. Stations
transmit the information about their queues status
and traffic classes to the AP and, based on this
information, AP coordinates access to the medium
between the stations.
B. IEEE 802.11 PHY Layer:
IEEE 802.11 Physical layer is the interface
between MAC layer and the air interface. The frame
exchange between Physical layer and MAC is under
the control of Physical Layer Convergence
Procedure (PLCP). Physical Layer is the entity in
charge of actual transmission using different
modulation schemes over the air interface. It also
informs the MAC layer about the activity status of
the medium. Currently, there are four standards
defining the physical layer: IEEE 802.11a, 802.11b,
802.11g and 802.11n. Among these, IEEE 802.11n
is the newest which is still under standardization. It
utilizes Multiple-Input-Multiple-Output (MIMO)
technology to achieve significantly higher rates. [8].
C. Propagation Models:
In this section, we explore both concepts of
Large-scale Path Loss and Fading. We introduce
three models of Large-scale Path Loss which
account for the large-scale attenuation of signal
based on distance: Free-Space, Two-Ray and
Lognormal Shadowing [6].
1) Free-Space Model:
This model is used to predict the signal
strength when the transmitter and the receiver have
a clear, unobstructed line-of sight (LOS) path
between them. It predicts that the received power
decays as a function of Transmitter-Receiver
distance raised to some power typically to the
second power [6].
2) Two-Ray Model:
This model, which is a more realistic
model than the Free-Space model, addresses the
case when we consider a ground reflected
propagation path between transmitter and receiver,
in addition to the direct LOS path. This model is
especially useful for predicting the received power
at large distances from the transmitter and when the
transmitter is installed relatively high above the
ground [6].
3) Log-normal Shadowing Model:
The average loss for a given distance is
expressed using a Path Loss Exponent. For taking
into account the fact that surrounding environmental
clutter can be very different at various locations
having the same Transmitter-Receiver distance,
another parameter is incorporated in the calculation
of path loss.

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D. Fading Model:
The term Fading is used to describe the
rapid fluctuations of the amplitudes, phases, or
multipath delays of a signal over a short period of
time or distance. It is caused by interference
between multiple versions of the transmitted signal
which arrive at the receiver at slightly different
times.
1) Fast Rayleigh fading model:
The fast Rayleigh fading model is a
statistical model to represent the fast variation of
signal amplitude at the receiver due to the motion of
the transmitter/receiver pair.
2) Rayleigh fading model:
Rayleigh fading model is a statistical
model to represent the fast variation of signal
amplitude at the receiver. In wireless propagation,
Rayleigh fading occurs when there is no line of sight
between the transmitter and receiver.
3) Ricean fading model:
This model can be used for scenarios where
there is line of sight communication and the line of
sight signal is the dominant signal seen at the
receiver [8].
III. AODV AND DSR REACTIVE
ROUTING PROTOCOL
There are two types routing protocols for
wireless networks, namely proactive and reactive. In
proactive routing, each node has one or more tables
that contain the latest information of the routes to
any other node in the network. Various table-driven
protocols differ in the way how the information
propagates through all nodes in the network when
topology changes. In reactive routing, route table is
set on demand and it maintains active routes only. If
a node wants to send a packet to another node then
reactive protocol searches for the route in an on-
demand manner and establishes the connection in
order to transmit and receive the packet. The route
discovery usually occurs by flooding the route
request packets throughout the network. Examples
of reactive routing protocols are the Dynamic
Source Routing (DSR), Ad-hoc On demand
Distance Vector routing (AODV) [12] [13].
A. AODV (Ad-hoc On demand Distance Vector
routing):
AODV is a flat routing protocol which
does not need any central administrative system to
handle the routing process. AODV tends to reduce
the control traffic messages overhead at the cost of
increased latency in finding new routes. AODV has
great advantage in having less overhead over simple
protocols. The RREQ and RREP messages which
are responsible for the route discovery do not
increase significantly the overhead from these
control messages. AODV reacts relatively quickly to
the topological changes in the network. It updates
the hosts that may be affected by the change, using
RERR message. The hello messages are responsible
for the route maintenance and are limited so that
they do not create unnecessary overhead in the
network. The AODV protocol is a loop free and
uses sequence numbers to avoid the infinity
counting problem which are typical to the classical
distance vector routing protocols.
Route Discovery in AODV When a node
wants to send a packet to some destination node and
does not have a valid route in its routing table for
that destination; it initiates a route discovery
process. Source node broadcasts a route request
(RREQ) packet to its neighbours, which then
forwards the request to their neighbours and so on
[9] [10]. Each node receiving the route request sends
a route back (Forward Path) to the node as shown in
the fig. 2
1
54
6
3
7
2
Source node
Destination Node
Route Request (RREQ)
Route Reply (RREP)
Route cache : 6-7
Figure2. Route discovery in AODV
B. DSR (DYNAMIC SOURCE ROUTING):
DSR is a fairly simple algorithm based on
the concept of source routing, in which a sending
node must provide the sequence of all nodes through
which a packet will travel. Each node maintains its
own route cache essentially in a routing table.
Source nodes determine routes dynamically and
only when needed. There are no periodic broadcasts
from routers. Fig.2 illustrates the DSR algorithm’s
route discovery/ route reply cycle. A source node
that wants to send a packet first checks its route
cache. If there is a valid entry for the
destination, the node sends the packet using that
route; if no valid route is available in the route
cache, the source node initiates the route discovery
process by sending a special route request (RREQ)
packet to all neighboring nodes. The RREQ
propagates through the network, collecting the
addresses of all nodes visited, until it reaches the
destination node or an intermediate node with a
valid route to the destination node. This node in
turn initiates the route reply process by sending a
special route reply (RREP) packet to the originating
node announcing the newly discovered route. The
destination node can accomplish this using inverse
routing or by initiating the route discovery process
backwards. The protocol can also easily be

1193 | P a g e
improved to support multiple routes to the same
destination. DSR’s main drawback is the large
bandwidth
[11].
Figure3. Dynamic source routing
C. Energy Consumption Model:
In our work we have assuming an energy
supply of 5V. The value corresponds to a 2,400MHz
Wave LAN implementation of IEEE 802.11e. When
a node sends and receives a packet, the network
interfaces of the node, decrements the available
energy according to the following parameters:
characteristics of network, the size of the packets
and the used bandwidth. The following equations
represent the energy used (in Joules) when a packet
is transmitted or received; packet size is represented
in bits:
Energy tx = (330*5*Packet size)/2*106
Energy rx = (230*5*Pocket size)/2*106
Although actual equipment consume
energy not only when sending and receiving but also
while listening, we assume in our model that the
listen operation is energy free, since all the
evaluated ad hoc routing protocols will have similar
energy consumption due to the node idle time.
Finally, note that when a packet is transmitted, a
percentage of the consumed energy represents the
Radio Frequency (RF) energy. This energy is used
for the propagation model in QualNet 5.0 to
determine the energy with which the neighbours’
interface nodes will receive the packet, and
consequently determine the successful or wrong
packet reception. In our simulation we maintain this
RF values in 281.8 mW, which corresponds to the
RF energy required to model a radio range of 250
meters [14].
1) Energy Consumption (mJoule):
The MICAZ Mote devices are in the
following four states: transmitting, receiving, idle
and sleep. Energy consumption is the quantity of
energy consumed by mote during the above
mentioned states of the device. The unit of energy
consumption used in the simulations is mJoule.
IV. SIMULATIONS AND RESULT
A. Designing of Scenario:
The aim of these simulations is to analyze
the selected routing protocols (DSR and AODV) for
their efficiency in terms of energy, throughput as
well as network life time. The basic methodology
consists of simulating with a basic scenario and then
by varying selected parameters, simulates the
generated scenarios. The selected parameters are
sources, pause time, nodes, area, sending rate and
mobility speed etc. The traffic sources used in the
simulations generated constant bit rate (CBR) data
traffic. The TCP sources are not being chosen
because it adapts to the load of the network. For the
same data traffic and movement scenario, the time
of sending the packet of a node will be different in
case of TCP, hence will become difficult to compare
the performance of different protocols. The energy
model taken is as used by. The values used
correspond to 2,400 MHZ Wave LAN
implementation of IEEE 802.11e. The radio
frequency value is set as 0.2818 W for transmission
range of 250 m [15].
1) Table 1 Simulation Parameters:
Channel type Wireless channel
Radio-propagation
model
Two Ray Ground
Antenna type Omni Antenna
Network interface type Phy/ Wireless Phy
MAC type IEEE 802.11e
Topological area 1500 x 1500 sq. m
Tx Power 1.65 W
Rx Power 1.15 W
Idle Power 1.0 W
Initial energy of a Node 1000.0 Joules
Routing protocols DSR/ AODV
Number of mobile nodes 40
Maximum speed 15 m/s
Rate 4 Packet/ s
Pause time 5,10,15,20, 25 sec.
Simulation time 500 sec.
Energy Model MICAZ
We have implemented the above table 1
parameters and compile the results to the EDCF. All
the simulations have been done using the QualNet
5.0 network simulator designed by Scalable
Networks Inc. For carrying out the simulation we
have taken 40 nodes in a 1500mX1500m area. The
random distribution model has been followed. The
nodes are fully independent and are operating in a
distributed environment [15].

1194 | P a g e
Figure4. Shows the outcome of running scenario of
IEEE 802.11e AODV routing protocol CBR.
B. Performance metrics:
Design and performance analysis of routing
protocols used for mobile ad hoc network (MANET)
is currently an active area of research. To judge the
merit of a routing protocol, one needs metrics both-
qualitative and quantitative- with which to measure
its suitability and performance metrics. Specifically,
this paper evaluates the performance comparison of
AODV and DSDV routing protocols with IEEE
802.11e MAC. The following performance metrics
are used to compare the performance of these
routing protocols in the simulation:
1) Average Jitter:
Figure5. Shows the Average jitter in application
layer of IEEE 802.11e AODV routing protocol
CBR.
Figure6. Shows the Average jitter in application
layer of IEEE 802.11e DSR routing protocol CBR.
In the above graph the effect of the average
jitter value is higher for DSR routing protocol as
compare to AODV in effect of EDFC. There is a
consistent improvement in the average jitter value
for any no. of packets sent.
2) Average End-To-End Delay:
Figure 7 Shows the Average End to End Delay (s) in
application layer of IEEE 802.11e AODV routing
protocol CBR
Figure 8 Shows the Average End to End Delay (s) in
application layer of IEEE 802.11e DSR routing
protocol CBR.
Average End-to-End delay (seconds) is the
average time it takes a data packet to reach the
destination. This metric is calculated by subtracting
time at which first packet was transmitted by source
from time at which first data packet arrived to
destination. This includes all possible delays caused
by buffering during route discovery latency, queuing
at the interface queue, retransmission delays at the
MAC, propagation and transfer times. This metric is
significant in understanding the delay introduced by
path discovery. The above Fig. shows the variation
comparisons of average end to end delay between
AODV and DSR routing protocols.
3) Throughput:
Figure. 9 Shows the Throughput (s) in application
CBR.
Figure 10 Shows the Throughput (s) in application
CBR.

1195 | P a g e
The throughput of the protocols can be defined as
percentage of the packets received by the destination
among the packets sent by the source. It is the
amount of data per time unit that is delivered from
one node to another via a communication link. The
throughput is measured in bits per second (bit/s or
bps).
4) Energy Consumed in Transmit Mode:
Figure 11 Energy Consumed (in mjoules) in
Transmit mode with variation in nodes in Physical
layer taking AODV routing protocol CBR.
Figure 12 Energy Consumed (in mjoules) in
Transmit mode with variation in nodes in Physical
layer taking DSR routing protocol CBR.
5) Energy Consumed in Receive Mode:
Figure 13 Energy Consumed (in mjoules) in receive
mode with variation in nodes in Physical layer
taking AODV routing protocol.
Figure 14 Energy Consumed (in mjoules) in receive
taking DSR routing protocol.
The energy consumption between source to
destination in receive mode is higher when the
collision rate increases between nodes. The
efficiency also decreased at high traffic conditions
collision rate.
6) Energy Consumed in Ideal Mode:
Figure 15 Energy Consumed (in mjoules) in ideal
taking AODV routing protocol.
Figure 16 Energy Consumed (in mjoules) in ideal
taking DSR routing protocol.
The performance of 802.11e EDCF in ideal
mode of energy consumption which supports the use
of both basic and RTS/CTS access mechanisms. The
collision contention period between nodes is very
less because of the AODV routing protocol
consumes less energy as compare to DSR in ideal
mode.
V. CONCLUSION
In this paper, we analysis the productivity
based performance metric analysis of IEEE 802.11e
which is contention-based channel access scheme
for QoS support, called the EDCF, of the emerging
802.11e MAC. Based on the simulation, we analysis
the productivity of inheritance the 802.11e EDCF to
show that the EDCF can provide differentiated
channel access among different energy priority
traffic. We also evaluated the performance metric
such as average Jitter, average throughput, End to
End delay, Energy consumed in transmit, receive
and ideal mode is shown above.
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1196 | P a g e
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AUTHORS:
DharamVir the M.Tech Degree form
MDU Rothak (Haryana) and B.E
Degree in Electronics and
Communication Engg.from Jamia
Millia Islamia, Central University,
New Delhi 2008, 2004 respectively.
He started his carrier as R&D Engineer in the field
of computers and networking, since 1992, he is the
part of YMCA University of Science & Technology
as Head of Section (Electronics & Inst. Control) in
the Department of Electronics Engineering. He is
pursuing Ph.D in the field of Power aware routing in
Mobile Ad hoc Networks.
Dr. S.K. Agarwal the M.Tech
Degree from Delhi Technical
University.New Delhi and PhD
degree in Electronics Engg. From
Jamia Millia Islamia, Central
University, New Delhi in 1998 and
2008, respectively,since 1990. He has been part of
YMCA University of Science & Technology
Faridabad (Haryana), as Dean and Chairman in
Department of Electronics Engineering.
Dr. Sayed Akthar Imam the B.E
Engg. degree (Electrical Engg)
from Jamia Millia Islamia, M.
Tech (Instrumentation & Control
System) from AMU, Aligarh and
PhD degree in Electronics &
Comm. Engg from Jamia Millia Islamia (a Central
University), New Delhi, in 1990, 1998, and 2008,
respectively. Since 1990, he has been part of Jamia
Millia Islamia University, where he is Assistant
Professor in the Department of Electronics and
Communication Engineering.

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